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The eukaryotic genome is organized in three dimensions within the nucleus. Transcriptionally active chromatin is spatially separated from silent heterochromatin, a large fraction of which is located at the nuclear periphery. However, the mechanisms by which chromatin is localized at the nuclear periphery remain poorly understood. Here we demonstrate that Proline Rich 14 (PRR14) protein specifically organizes H3K9me3-modified heterochromatin at the nuclear lamina. We show that PRR14 dynamically associates with both the nuclear lamina and heterochromatin, and is able to reorganize heterochromatin in the nucleus of interphase cells independent of mitosis. We demonstrate that PRR14 can bind all isoforms of heterochromatin protein 1 (HP1). We characterize two functional HP1-binding sites within PRR14 that contribute to its association with heterochromatin. Results of fluorescent recovery after photobleaching (FRAP) and super-resolution imaging indicate that PPR14 forms an anchoring surface for heterochromatin at the nuclear lamina where it interacts dynamically with HP1-associated, H3K9me3-modified chromatin. Our study reveals the mechanism through which PRR14 tethers heterochromatin to the nuclear lamina and we propose a model of dynamic heterochromatin organization at the nuclear periphery.

Additional file 2: The images of the untrimmed western blots.

Additional file: Supplementary Tables. Table S1. Regulatory annotation of TNFRSF13B rs4792800 and its linked variants. Table S2. List of differentially expressed genes upon TNFRSF13B knockdown that overlapped in both 22Rv1 and PC-3 human prostate cancer cells. Table S3. List of differentially expressed cell cycle- and p53 signalling pathway-related genes with opposite responses to TNFRSF13B knockdown between 22Rv1 and PC-3 cells.

Catalytic carbon dioxide (CO2) hydrogenation to carbon monoxide (CO) via reverse water-gas shift (RWGS) reaction is of particular interest due to its direct use in various industrial processes as feedstock. However, the competitive CO2 methanation process severely limits the RWGS reaction in a lower temperature range. In this context, we propose a novel nanocatalyst (NC) comprising oxygen vacancy-enriched subnanometer-scale CoPd hybrid cluster (CoOxVPd)-anchored Pd nanoparticles (NPs) on cobalt oxide support underneath (denoted as CP-CoOxVPd) by using a galvanic replacement reaction-assisted wet chemical reduction method. As-developed CP-CoOxVPd NC initiated the RWGS reaction at 423 K temperature while showing an optimum CO production yield of ∼3414 μmol g−1catalyst and a CO selectivity as high as ∼99% at 523 K in the reaction gas of CO2:H2 = 1:3. The results of physical characterizations along with electrochemical and gas chromatography (GC) suggest that abundant oxygen vacancies in the surface-anchored CoOxVPd clusters are vital for CO2 adsorption and subsequent activation, while neighboring Pd domains facilitate the H2 dissociation. The obtained results are expected to provide a feasible design of Co-based NCs for the RWGS reaction.

Severe corneal injuries often result in permanent vision loss and remain a clinical challenge. Human bone marrow-derived mesenchymal stem cells (MSCs) and their secreted factors (secretome) have been studied for their antiscarring, anti-inflammatory, and antiangiogeneic properties. We aimed to deliver lyophilized MSC secretome (MSC-S) within a viscoelastic gel composed of hyaluronic acid (HA) and chondroitin sulfate (CS) as a way to enhance corneal re-epithelialization and reduce complications after mechanical and chemical injuries of the cornea. We hypothesized that delivering MSC-S within HA/CS would have improved wound healing effects compared the with either MSC-S or HA/CS alone. The results showed that a once-daily application of MSC-S in HA/CS enhances epithelial cell proliferation and wound healing after injury to the cornea. It also reduced scar formation, neovascularization, and hemorrhage after alkaline corneal burns. We found that combining MSC-S and HA/CS increased the expression of CD44 receptors colocalized with HA, suggesting that the observed therapeutic effects between the MSC-S and HA/CS are in part mediated by CD44 receptor upregulation and activation by HA. The results from this study demonstrate a reproducible and efficient approach for delivering the MSC-S to the ocular surface for treatment of severe corneal injuries. Stem Cells Translational Medicine 2019;8:478–489

Prostate and breast cancers are hormone-related malignancies and are characterized by a complex interplay of hundreds of susceptibility loci throughout the genome. Prostate cancer could be inhibited by eliminating androgens through castration or estrogen administration, thus facilitating long-term treatment of prostate cancer; however, the role of estrogen in prostate cancer remains unclear. This study aimed to determine whether polygenic risk scores (PRSs) comprising combinations of genome-wide susceptibility variants influence the clinical outcomes of prostate cancer patients. The study subjects were recruited from four medical centers in Taiwan, and genome-wide genotyping data were obtained from 643 prostate cancer patients. We derived the PRS for prostate cancer (PRS-PC) and for breast cancer (PRS-BC) for each patient. The association between the PRS-PC/PRS-BC at the age of prostate cancer onset and recurrence within seven years was evaluated using a regression model adjusted for population stratification components. A higher PRS-PC was associated with an earlier onset age for prostate cancer (beta in per SD increase in PRS = -0.89, P = 0.0008). In contrast, a higher PRS-BC was associated with an older onset age for prostate cancer (beta = 0.59, P = 0.02). PRS-PC was not associated with the risk of recurrence (hazard ratio = 1.03, P = 0.67), whereas a higher PRS-BC was associated with a low recurrence risk (hazard ratio = 0.86, P = 0.03). These results indicate that the genetic predisposition to breast cancer is associated with a low risk of prostate cancer recurrence. Further studies are warranted to explore the role of breast cancer susceptibility variants and estrogen signaling in prostate cancer progression.

BACKGROUND Early recognition of severely injured patients in prehospital settings is of paramount importance for timely treatment and transportation of patients to further treatment facilities. The dispatching accuracy has seldom been addressed in previous studies. OBJECTIVE In this study, we aimed to build a machine learning–based model through text mining of emergency calls for the automated identification of severely injured patients after a road accident. METHODS Audio recordings of road accidents in Taipei City, Taiwan, in 2018 were obtained and randomly sampled. Data on call transfers or non-Mandarin speeches were excluded. To predict cases of severe trauma identified on-site by emergency medical technicians, all included cases were evaluated by both humans (6 dispatchers) and a machine learning model, that is, a prehospital-activated major trauma (PAMT) model. The PAMT model was developed using term frequency–inverse document frequency, rule-based classification, and a Bernoulli naïve Bayes classifier. Repeated random subsampling cross-validation was applied to evaluate the robustness of the model. The prediction performance of dispatchers and the PAMT model, in severe cases, was compared. Performance was indicated by sensitivity, specificity, positive predictive value, negative predictive value, and accuracy. RESULTS Although the mean sensitivity and negative predictive value obtained by the PAMT model were higher than those of dispatchers, they obtained higher mean specificity, positive predictive value, and accuracy. The mean accuracy of the PAMT model, from certainty level 0 (lowest certainty) to level 6 (highest certainty), was higher except for levels 5 and 6. The overall performances of the dispatchers and the PAMT model were similar; however, the PAMT model had higher accuracy in cases where the dispatchers were less certain of their judgments. CONCLUSIONS A machine learning–based model, called the PAMT model, was developed to predict severe road accident trauma. The results of our study suggest that the accuracy of the PAMT model is not superior to that of the participating dispatchers; however, it may assist dispatchers when they lack confidence while making a judgment.

Background Early recognition of severely injured patients in prehospital settings is of paramount importance for timely treatment and transportation of patients to further treatment facilities. The dispatching accuracy has seldom been addressed in previous studies. Objective In this study, we aimed to build a machine learning–based model through text mining of emergency calls for the automated identification of severely injured patients after a road accident. Methods Audio recordings of road accidents in Taipei City, Taiwan, in 2018 were obtained and randomly sampled. Data on call transfers or non-Mandarin speeches were excluded. To predict cases of severe trauma identified on-site by emergency medical technicians, all included cases were evaluated by both humans (6 dispatchers) and a machine learning model, that is, a prehospital-activated major trauma (PAMT) model. The PAMT model was developed using term frequency–inverse document frequency, rule-based classification, and a Bernoulli naïve Bayes classifier. Repeated random subsampling cross-validation was applied to evaluate the robustness of the model. The prediction performance of dispatchers and the PAMT model, in severe cases, was compared. Performance was indicated by sensitivity, specificity, positive predictive value, negative predictive value, and accuracy. Results Although the mean sensitivity and negative predictive value obtained by the PAMT model were higher than those of dispatchers, they obtained higher mean specificity, positive predictive value, and accuracy. The mean accuracy of the PAMT model, from certainty level 0 (lowest certainty) to level 6 (highest certainty), was higher except for levels 5 and 6. The overall performances of the dispatchers and the PAMT model were similar; however, the PAMT model had higher accuracy in cases where the dispatchers were less certain of their judgments. Conclusions A machine learning–based model, called the PAMT model, was developed to predict severe road accident trauma. The results of our study suggest that the accuracy of the PAMT model is not superior to that of the participating dispatchers; however, it may assist dispatchers when they lack confidence while making a judgment.

ObjectivesThe healthcare profession has been long considered an excellent career choice. Pre-medical experience is documented to be important in shaping future medical landscape. In the wake of the pandemic, there has been intense media spotlight on the healthcare profession and change in academic environment, necessitating analyses of student experience. This project aims to assess change in undergraduate student interest in healthcare career using cross-sectional survey study.MethodsThe project was approved by our Institutional Review Board. Voluntary survey collected data on demographics, socioeconomics, media exposure, academic environment, and change in interest in a healthcare profession. Survey was distributed through the university undergraduate pre-health listserv. Total of 297 responses were obtained. Descriptive statistics including Fisher’s exact test were applied in the analysis.ResultsMajority of the respondents were Asians (54.9%), second generation immigrants (52.2%), and female (73.4%). Large proportion of the respondents were negatively affected by the pandemic, with losing a job or internship personally (42.1%) or a family member or a friend (62.6%). Students had mixed response to online learning environment, with 27.3% of students noting no change, 40.4% students noting increased difficulty, and 32.3% students noting decreased difficulty of classes. During the pandemic, 47.5% of students noted increased interest in pursuing healthcare career. The change in interest in healthcare career was not associated with xdemographics, economic hardship, or online learning environment.DiscussionDespite the challenges of COVID-19 pandemic, students showed strong interest in pursuing healthcare careers.